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<a href="brain_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="preprocessor">#include &quot;<a class="code" href="brain_8hpp.html">brain.hpp</a>&quot;</span>
<a name="l00002"></a>00002 <span class="preprocessor">#include &quot;<a class="code" href="neuron_8hpp.html">neuron.hpp</a>&quot;</span>
<a name="l00003"></a>00003 
<a name="l00004"></a>00004 <span class="keyword">using namespace </span>std;
<a name="l00005"></a>00005 
<a name="l00006"></a><a class="code" href="class_brain.html#a0e997fc498f92313d7af89ece7cef4fa">00006</a> <a class="code" href="class_brain.html#a92daf9737361454abbb5988a89b7ff40">Brain::Brain</a> (<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> nInputs, <a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> nNeuronsPerLayer, <a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> nLayers, <a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> nOutputs) {
<a name="l00007"></a>00007     m_nInputs = nInputs;
<a name="l00008"></a>00008     m_nNeurons = nNeuronsPerLayer * nLayers;
<a name="l00009"></a>00009     m_nOutputs = nOutputs;
<a name="l00010"></a>00010     m_nNeuronsPerLayer = nNeuronsPerLayer;
<a name="l00011"></a>00011     m_inputNeurons = <span class="keyword">new</span> <a class="code" href="class_neuron.html">Neuron</a>[m_nInputs];
<a name="l00012"></a>00012     m_bodyNeurons = <span class="keyword">new</span> <a class="code" href="class_neuron.html">Neuron</a>[m_nNeurons];
<a name="l00013"></a>00013     m_outputNeurons = <span class="keyword">new</span> <a class="code" href="class_neuron.html">Neuron</a>[m_nOutputs];
<a name="l00014"></a>00014 
<a name="l00015"></a>00015     <span class="comment">//cout&lt;&lt;nInputs&lt;&lt;&quot; &quot;&lt;&lt;nNeuronsPerLayer&lt;&lt;&quot; &quot;&lt;&lt;nLayers&lt;&lt;&quot; &quot;&lt;&lt;nOutputs&lt;&lt;endl;</span>
<a name="l00016"></a>00016 
<a name="l00017"></a>00017     <span class="comment">//1. Init the number of required connections for each Neuron</span>
<a name="l00018"></a>00018     <span class="comment">//2. Set the connections for body (minus first layer) and output Neurons (the last 2 for loops)</span>
<a name="l00019"></a>00019     <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=0; i&lt;m_nInputs; i++){
<a name="l00020"></a>00020         m_inputNeurons[i].<a class="code" href="class_neuron.html#a471ac0d115eb2c16b3a4278f9d8f88e5">init</a>(0,0);
<a name="l00021"></a>00021     }
<a name="l00022"></a>00022     <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=0; i&lt;m_nNeuronsPerLayer; i++){
<a name="l00023"></a>00023         m_bodyNeurons[i].init(m_nInputs,0);
<a name="l00024"></a>00024 
<a name="l00025"></a>00025         <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> j=0; j&lt;m_nInputs; j++){
<a name="l00026"></a>00026             m_bodyNeurons[i].getConnections()[j].pNeuron = &amp; m_inputNeurons[j];
<a name="l00027"></a>00027             m_bodyNeurons[i].getConnections()[j].weight = rand()%1000/500.0 - 1.0;
<a name="l00028"></a>00028         }
<a name="l00029"></a>00029     }
<a name="l00030"></a>00030     <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=m_nNeuronsPerLayer; i&lt;m_nNeurons; i+=m_nNeuronsPerLayer){
<a name="l00031"></a>00031         <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> p=0; p&lt;m_nNeuronsPerLayer; p++){
<a name="l00032"></a>00032             m_bodyNeurons[i+p].init(m_nNeuronsPerLayer, 0);
<a name="l00033"></a>00033             <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> j=0; j&lt;m_nNeuronsPerLayer; j++){
<a name="l00034"></a>00034                 m_bodyNeurons[i+p].getConnections()[j].pNeuron = &amp;m_bodyNeurons[i+p - m_nNeuronsPerLayer];
<a name="l00035"></a>00035                 m_bodyNeurons[i+p].getConnections()[j].weight = rand()%1000/500.0 - 1.0;
<a name="l00036"></a>00036             }
<a name="l00037"></a>00037         }
<a name="l00038"></a>00038     }
<a name="l00039"></a>00039     <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=0; i&lt;m_nOutputs; i++){
<a name="l00040"></a>00040         m_outputNeurons[i].init(m_nNeuronsPerLayer, 0);
<a name="l00041"></a>00041         <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> j=0; j&lt;m_nNeuronsPerLayer; j++){
<a name="l00042"></a>00042             m_outputNeurons[i].getConnections()[j].pNeuron = &amp;m_bodyNeurons[m_nNeurons - m_nNeuronsPerLayer + j];
<a name="l00043"></a>00043             m_outputNeurons[i].getConnections()[j].weight = rand()%1000/500.0 - 1.0;
<a name="l00044"></a>00044         }
<a name="l00045"></a>00045     }
<a name="l00046"></a>00046 }
<a name="l00047"></a>00047 
<a name="l00048"></a><a class="code" href="class_brain.html#af41db0fbf20565d17d36bad299ae0cac">00048</a> <a class="code" href="class_brain.html#af41db0fbf20565d17d36bad299ae0cac">Brain::~Brain</a> () {
<a name="l00049"></a>00049     <span class="keyword">delete</span>[] m_inputNeurons;
<a name="l00050"></a>00050     <span class="keyword">delete</span>[] m_bodyNeurons;
<a name="l00051"></a>00051     <span class="keyword">delete</span>[] m_outputNeurons;
<a name="l00052"></a>00052 }
<a name="l00053"></a>00053 
<a name="l00054"></a><a class="code" href="class_brain.html#ac80876d3cd168b2f638c49d03787ff22">00054</a> <span class="keywordtype">void</span> <a class="code" href="class_brain.html#ac80876d3cd168b2f638c49d03787ff22">Brain::compute</a>(<span class="keywordtype">float</span> * inputs, <span class="keywordtype">float</span> * outputs){
<a name="l00055"></a>00055     <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=0; i&lt;m_nInputs; i++){
<a name="l00056"></a>00056         m_inputNeurons[i].setState(inputs[i]);
<a name="l00057"></a>00057     }
<a name="l00058"></a>00058     <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=0; i&lt;m_nNeurons; i++){
<a name="l00059"></a>00059         m_bodyNeurons[i].compute();
<a name="l00060"></a>00060     }
<a name="l00061"></a>00061     <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=0; i&lt;m_nOutputs; i++){
<a name="l00062"></a>00062         m_outputNeurons[i].compute();
<a name="l00063"></a>00063         outputs[i] = m_outputNeurons[i].getState();
<a name="l00064"></a>00064     }
<a name="l00065"></a>00065 }
<a name="l00066"></a>00066 
<a name="l00067"></a><a class="code" href="class_brain.html#a843fd88f56948142b1934d25f8d16089">00067</a> <span class="keywordtype">int</span> <a class="code" href="class_brain.html#a843fd88f56948142b1934d25f8d16089">Brain::copy</a> (<a class="code" href="class_brain.html">Brain</a> * source) {
<a name="l00068"></a>00068     <span class="keywordflow">return</span> 1;
<a name="l00069"></a>00069 }
<a name="l00070"></a>00070 
<a name="l00071"></a><a class="code" href="class_brain.html#a8d9c671760b41bd018b368fed8de928e">00071</a> <span class="keywordtype">void</span> <a class="code" href="class_brain.html#a8d9c671760b41bd018b368fed8de928e">Brain::mutate</a> (<span class="keywordtype">int</span> rate) {
<a name="l00072"></a>00072     <span class="keywordflow">for</span> (<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i = 0; i &lt; m_nNeurons; i++) {
<a name="l00073"></a>00073         m_bodyNeurons[i].mutate(rate);
<a name="l00074"></a>00074     }
<a name="l00075"></a>00075 }
<a name="l00076"></a>00076 
<a name="l00077"></a><a class="code" href="class_brain.html#acf2c5a7b3a26c74236c14207b995bfe2">00077</a> <span class="keywordtype">void</span> <a class="code" href="class_brain.html#acf2c5a7b3a26c74236c14207b995bfe2">Brain::crossover</a> (<a class="code" href="class_brain.html">Brain</a> * parent1, <a class="code" href="class_brain.html">Brain</a> * parent2) {
<a name="l00078"></a>00078     <span class="comment">/* TODO: implement */</span>
<a name="l00079"></a>00079 
<a name="l00080"></a>00080 }
<a name="l00081"></a>00081 
<a name="l00082"></a><a class="code" href="class_brain.html#a6127669e8f3abf0b0c0701b4c8ccca89">00082</a> <span class="keywordtype">void</span> <a class="code" href="class_brain.html#a6127669e8f3abf0b0c0701b4c8ccca89">Brain::saveBrain</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> * filename){
<a name="l00083"></a>00083     ofstream brainFile;
<a name="l00084"></a>00084     brainFile.open(filename, ios::out | ios::trunc );
<a name="l00085"></a>00085 
<a name="l00086"></a>00086     <span class="keywordflow">if</span>(brainFile.is_open()){
<a name="l00087"></a>00087         <span class="comment">//Number of Connections on a neuron in first layer</span>
<a name="l00088"></a>00088         brainFile &lt;&lt; m_bodyNeurons[0].getNumOfConn() &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>;
<a name="l00089"></a>00089 
<a name="l00090"></a>00090         <span class="comment">//Number of Connections on a neuron after first layer</span>
<a name="l00091"></a>00091         brainFile &lt;&lt; m_bodyNeurons[m_nNeuronsPerLayer].getNumOfConn() &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>;
<a name="l00092"></a>00092 
<a name="l00093"></a>00093         <span class="comment">//First Layer</span>
<a name="l00094"></a>00094         <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=0; i&lt;m_nNeuronsPerLayer; i++){
<a name="l00095"></a>00095             <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> j=0; j&lt;m_nInputs; j++){
<a name="l00096"></a>00096                 brainFile &lt;&lt; m_bodyNeurons[i].getConnections()[j].weight &lt;&lt; <span class="stringliteral">&quot; &quot;</span>;
<a name="l00097"></a>00097             }
<a name="l00098"></a>00098         }
<a name="l00099"></a>00099         brainFile &lt;&lt; <span class="stringliteral">&quot;\nDoneFirstLayer\n&quot;</span>;
<a name="l00100"></a>00100 
<a name="l00101"></a>00101         <span class="comment">//Body Neurons</span>
<a name="l00102"></a>00102         <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=m_nNeuronsPerLayer; i&lt;m_nNeurons; i++){
<a name="l00103"></a>00103             <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> j=0; j&lt;m_nNeuronsPerLayer; j++){
<a name="l00104"></a>00104                 brainFile &lt;&lt; m_bodyNeurons[i].getConnections()[j].weight &lt;&lt; <span class="stringliteral">&quot; &quot;</span>;
<a name="l00105"></a>00105             }
<a name="l00106"></a>00106             brainFile&lt;&lt;<span class="stringliteral">&quot;\n&quot;</span>;
<a name="l00107"></a>00107         }
<a name="l00108"></a>00108 
<a name="l00109"></a>00109         <span class="comment">//Output Neurons</span>
<a name="l00110"></a>00110         <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> i=0; i&lt;m_nOutputs; i++){
<a name="l00111"></a>00111             <span class="keywordflow">for</span>(<a class="code" href="types_8hpp.html#a91ad9478d81a7aaf2593e8d9c3d06a14">uint</a> j=0; j&lt;m_nNeuronsPerLayer; j++){
<a name="l00112"></a>00112                 brainFile &lt;&lt; m_outputNeurons[i].getConnections()[j].weight &lt;&lt; <span class="stringliteral">&quot; &quot;</span>;
<a name="l00113"></a>00113             }
<a name="l00114"></a>00114         }
<a name="l00115"></a>00115         brainFile.close();
<a name="l00116"></a>00116     }
<a name="l00117"></a>00117 }
<a name="l00118"></a>00118 
<a name="l00119"></a><a class="code" href="class_brain.html#ae8e1470ecdf8b573f2c04fd45ff65560">00119</a> <span class="keywordtype">void</span> <a class="code" href="class_brain.html#ae8e1470ecdf8b573f2c04fd45ff65560">Brain::loadBrain</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> * filename){
<a name="l00120"></a>00120     <span class="keywordtype">string</span> line;
<a name="l00121"></a>00121     stringstream sstr;
<a name="l00122"></a>00122     ifstream brainFile;
<a name="l00123"></a>00123     brainFile.open(filename, ios::in );
<a name="l00124"></a>00124     <span class="keywordflow">if</span>(brainFile.is_open()){
<a name="l00125"></a>00125         <span class="keywordflow">while</span>(brainFile.good()){
<a name="l00126"></a>00126             getline(brainFile, line);
<a name="l00127"></a>00127 
<a name="l00128"></a>00128         }
<a name="l00129"></a>00129         brainFile.close();
<a name="l00130"></a>00130     }<span class="keywordflow">else</span>{
<a name="l00131"></a>00131         cout &lt;&lt; <span class="stringliteral">&quot;Unable to open file&quot;</span> &lt;&lt; endl;
<a name="l00132"></a>00132     }
<a name="l00133"></a>00133 
<a name="l00134"></a>00134 }
</pre></div></div><!-- contents -->


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